1 Introduction

The purpose of this notebook is to cluster and annotate the cells obtained from patient with id 63.

2 Pre-processing

2.1 Load packages

library(Seurat)
library(ggpubr)
library(tidyverse)

2.2 Parameters

# Paths
path_to_obj <- here::here("results/R_objects/patient_63/2.seurat_object_filtered.rds")
path_to_save <- here::here("results/R_objects/patient_63/3.seurat_annotated.rds")
path_to_save_markers <- here::here("3-clustering_and_annotation/tmp/markers_clusters_63.rds")


# Functions
source(here::here("bin/utils.R"))


# Params
k_param <- 10
min_log2FC <- 0.3
alpha <- 0.001

2.3 Load data

seurat <- readRDS(path_to_obj)
seurat
## An object of class Seurat 
## 13680 features across 983 samples within 1 assay 
## Active assay: RNA (13680 features, 0 variable features)

3 Dimensionality reduction

3.1 All cells

3.1.1 Data normalization

seurat <- NormalizeData(
  seurat,
  normalization.method = "LogNormalize",
  scale.factor = 10000
)

3.1.2 Highly variable genes

seurat <- FindVariableFeatures(seurat)
LabelPoints(
  plot = VariableFeaturePlot(seurat),
  points = head(VariableFeatures(seurat), 10),
  repel = TRUE
)

3.1.3 Principal Component Analysis

seurat <- ScaleData(seurat)
seurat <- RunPCA(seurat)
VizDimLoadings(seurat, dims = 1:2, reduction = "pca")

3.1.4 UMAP

seurat <- RunUMAP(seurat, reduction = "pca", dims = 1:20)
umap_time_point <- DimPlot(seurat, group.by = "time_point")
umap_tissue <- DimPlot(seurat, group.by = "tissue")
umap_time_point + umap_tissue

4 Cell Cycle Score

As upregulation of cell cycle genes is a hallmark of Richter transformation, we will infer the cell cycle score and phase for each cell:

seurat <- CellCycleScoring(
  seurat,
  s.features = cc.genes.updated.2019$s.genes,
  g2m.features = cc.genes.updated.2019$g2m.genes,
  set.ident = FALSE
)
DimPlot(seurat, group.by = "Phase")

umap_s_score <- FeaturePlot(seurat, features = "S.Score") +
  scale_color_viridis_c(option = "magma") +
  labs(title = "S Score") +
  theme(
    plot.title = element_text(hjust = 0.5, size = 12, face = "plain"),
    axis.title = element_blank(),
    axis.text = element_blank(),
    axis.ticks = element_blank(),
    axis.line = element_blank()
  )
umap_g2m_score <- FeaturePlot(seurat, features = "G2M.Score") +
  scale_color_viridis_c(option = "magma") +
  labs(title = "G2M Score") +
  theme(
    plot.title = element_text(hjust = 0.5, size = 12, face = "plain"),
    axis.title = element_blank(),
    axis.text = element_blank(),
    axis.ticks = element_blank(),
    axis.line = element_blank()
  )
umap_cc_combined <- ggpubr::ggarrange(
  plotlist = list(umap_s_score, umap_g2m_score),
  nrow = 2,
  ncol = 1,
  common.legend = FALSE
)
umap_cc_combined

5 Cluster

seurat <- FindNeighbors(
  seurat,
  k.param = k_param,
  dims = 1:20,
  reduction = "pca"
)
seurat <- FindClusters(seurat, resolution = 0.2)
## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
## 
## Number of nodes: 983
## Number of edges: 19317
## 
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.8705
## Number of communities: 2
## Elapsed time: 0 seconds
DimPlot(seurat)

Let us subcluster to find the subpopulation of cycling cells

seurat <- FindSubCluster(
  seurat,
  cluster = "1",
  graph.name = "RNA_snn",
  subcluster.name = "subcluster_proliferative",
  resolution = 0.3
)
## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
## 
## Number of nodes: 223
## Number of edges: 4807
## 
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.7026
## Number of communities: 2
## Elapsed time: 0 seconds
DimPlot(seurat, group.by = "subcluster_proliferative")

6 Markers

seurat$final_clusters <- seurat$subcluster_proliferative
Idents(seurat) <- "final_clusters"
markers <- FindAllMarkers(seurat, only.pos = TRUE, logfc.threshold = min_log2FC)
markers <- markers %>%
  mutate(cluster = as.character(cluster)) %>%
  dplyr::filter(p_val_adj < alpha) %>%
  arrange(cluster) %>%
  group_by(cluster) %>%
  arrange(desc(avg_log2FC), .by_group = TRUE)
DT::datatable(markers)

7 Annotation

Cluster Markers Annotation
0 CXCR4 CLL-like
1_0 TCL1A, BTK, WNT3 RT-like quiescent
1_1 TOP2A, PCNA RT-like proliferative 
seurat$annotation_final <- factor(
  seurat$final_clusters,
  levels = c("0", "1_0", "1_1"),
)
new_levels_63 <- c("CLL-like", "RT-like quiescent", "RT-like proliferative")
levels(seurat$annotation_final) <- new_levels_63
reordered_levels_63 <- new_levels_63
seurat$annotation_final <- factor(seurat$annotation_final, reordered_levels_63)
Idents(seurat) <- "annotation_final"


# Plot UMAP
cols <- c("gray79", "#f6c7c4", "#6d203f")
names(cols) <- levels(seurat$annotation_final)
umap_annotation <- DimPlot(seurat, pt.size = 1)
col_labels <- c(
  "CLL-like" = bquote("CLL-like"),
  "RT-like quiescent" = bquote("RT-like quiescent"),
  "RT-like proliferative" = bquote("RT-like proliferative")
)
umap_annotation <- umap_annotation +
  scale_color_manual(values = cols, breaks = names(cols), labels = col_labels) +
  theme(
    axis.title = element_blank(),
    axis.text = element_blank(),
    axis.ticks = element_blank(),
    axis.line = element_blank()
  )
umap_annotation

8 Visualize markers

# UMAPs
genes_interest <- c("CXCR4", "TCL1A", "BTK", "WNT3", "TOP2A", "PCNA")
feature_plots <- purrr::map(genes_interest, function(x) {
  p <- FeaturePlot(seurat, x, pt.size = 1) +
    scale_color_viridis_c(option = "magma")
  p
})
feature_plots
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# Dot plots
dot_plot <- DotPlot(seurat, features = rev(genes_interest)) +
  coord_flip() +
  scale_color_viridis_c(option = "magma") +
  scale_y_discrete(breaks = names(col_labels), labels = col_labels) +
  theme(
    axis.title = element_blank(),
    axis.text.x = element_text(angle = 45, vjust = 1, hjust=1),
    legend.title = element_text(size = 12)
  )
dot_plot

# Violin plots
vln_plot_s <- seurat@meta.data %>%
  ggplot(aes(annotation_final, S.Score)) +
    geom_violin(fill = "gray") +
    labs(x = "", y = "S Phase Score") +
    scale_x_discrete(breaks = names(col_labels), labels = col_labels) +
    theme_bw() +
    theme(axis.text.x = element_text(color = "black", angle = 45, vjust = 1, hjust = 1, size = 11))
vln_plot_s

vln_plot_g2m <- seurat@meta.data %>%
  ggplot(aes(annotation_final, G2M.Score)) +
    geom_violin(fill = "gray") +
    labs(x = "", y = "G2M Phase Score") +
    scale_x_discrete(breaks = names(col_labels), labels = col_labels) +
    theme_bw() +
    theme(axis.text.x = element_text(color = "black", angle = 45, vjust = 1, hjust = 1, size = 11))
vln_plot_g2m

9 Save

# Save Seurat object
saveRDS(seurat, path_to_save)


# Save markers
markers$annotation <- factor(markers$cluster)
levels(markers$annotation) <- new_levels_63
markers_list <- purrr::map(levels(markers$annotation), function(x) {
  df <- markers[markers$annotation == x, ]
  df <- df[, c(7, 1, 5, 2:4, 6, 8)]
  df
})
names(markers_list) <- levels(markers$annotation)
markers_list <- markers_list[reordered_levels_63]
saveRDS(markers_list, path_to_save_markers)
openxlsx::write.xlsx(
  x = markers_list,
  file = here::here("results/tables/markers/markers_annotated_clusters_patient_63.xlsx")
)

10 Session Information

sessionInfo()
## R version 4.0.4 (2021-02-15)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.2 LTS
## 
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C               LC_TIME=es_ES.UTF-8        LC_COLLATE=en_US.UTF-8     LC_MONETARY=es_ES.UTF-8    LC_MESSAGES=en_US.UTF-8    LC_PAPER=es_ES.UTF-8       LC_NAME=C                  LC_ADDRESS=C               LC_TELEPHONE=C             LC_MEASUREMENT=es_ES.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] forcats_0.5.1      stringr_1.4.0      dplyr_1.0.6        purrr_0.3.4        readr_1.4.0        tidyr_1.1.3        tibble_3.1.2       tidyverse_1.3.1    ggpubr_0.4.0       ggplot2_3.3.3      SeuratObject_4.0.2 Seurat_4.0.3       BiocStyle_2.18.1  
## 
## loaded via a namespace (and not attached):
##   [1] readxl_1.3.1          backports_1.2.1       plyr_1.8.6            igraph_1.2.6          lazyeval_0.2.2        splines_4.0.4         crosstalk_1.1.1       listenv_0.8.0         scattermore_0.7       digest_0.6.27         htmltools_0.5.1.1     fansi_0.5.0           magrittr_2.0.1        tensor_1.5            cluster_2.1.1         ROCR_1.0-11           limma_3.46.0          openxlsx_4.2.3        globals_0.14.0        modelr_0.1.8          matrixStats_0.59.0    spatstat.sparse_2.0-0 colorspace_2.0-1      rvest_1.0.0           ggrepel_0.9.1         haven_2.4.1           xfun_0.23             crayon_1.4.1          jsonlite_1.7.2        spatstat.data_2.1-0   survival_3.2-10       zoo_1.8-9             glue_1.4.2            polyclip_1.10-0       gtable_0.3.0          leiden_0.3.8          car_3.0-10            future.apply_1.7.0    abind_1.4-5           scales_1.1.1          DBI_1.1.1             rstatix_0.7.0         miniUI_0.1.1.1        Rcpp_1.0.6            viridisLite_0.4.0     xtable_1.8-4          reticulate_1.20       spatstat.core_2.1-2   foreign_0.8-81        DT_0.18               htmlwidgets_1.5.3     httr_1.4.2            RColorBrewer_1.1-2    ellipsis_0.3.2       
##  [55] ica_1.0-2             farver_2.1.0          pkgconfig_2.0.3       sass_0.4.0            uwot_0.1.10           dbplyr_2.1.1          deldir_0.2-10         here_1.0.1            utf8_1.2.1            labeling_0.4.2        tidyselect_1.1.1      rlang_0.4.11          reshape2_1.4.4        later_1.2.0           munsell_0.5.0         cellranger_1.1.0      tools_4.0.4           cli_2.5.0             generics_0.1.0        broom_0.7.7           ggridges_0.5.3        evaluate_0.14         fastmap_1.1.0         yaml_2.2.1            goftest_1.2-2         fs_1.5.0              knitr_1.33            fitdistrplus_1.1-5    zip_2.2.0             RANN_2.6.1            pbapply_1.4-3         future_1.21.0         nlme_3.1-152          mime_0.10             xml2_1.3.2            rstudioapi_0.13       compiler_4.0.4        plotly_4.9.4          curl_4.3.1            png_0.1-7             ggsignif_0.6.2        spatstat.utils_2.2-0  reprex_2.0.0          bslib_0.2.5.1         stringi_1.6.2         highr_0.9             RSpectra_0.16-0       lattice_0.20-41       Matrix_1.3-4          vctrs_0.3.8           pillar_1.6.1          lifecycle_1.0.0       BiocManager_1.30.15   spatstat.geom_2.1-0  
## [109] lmtest_0.9-38         jquerylib_0.1.4       RcppAnnoy_0.0.18      data.table_1.14.0     cowplot_1.1.1         irlba_2.3.3           httpuv_1.6.1          patchwork_1.1.1       R6_2.5.0              bookdown_0.22         promises_1.2.0.1      KernSmooth_2.23-18    gridExtra_2.3         rio_0.5.26            parallelly_1.26.0     codetools_0.2-18      MASS_7.3-53.1         assertthat_0.2.1      rprojroot_2.0.2       withr_2.4.2           sctransform_0.3.2     mgcv_1.8-36           parallel_4.0.4        hms_1.1.0             grid_4.0.4            rpart_4.1-15          rmarkdown_2.8         carData_3.0-4         Rtsne_0.15            shiny_1.6.0           lubridate_1.7.10